EdgeNet : Encoder-decoder generative Network for Auction Design in E-commerce Online Advertising
This addresses auction design for e-commerce advertising platforms, but it appears incremental as it extends existing neural auction frameworks.
The paper tackles the problem of auction design in e-commerce online advertising by proposing EdgeNet, an encoder-decoder generative network that breaks the neural auction paradigm of Generalized-Second-Price (GSP) to improve data utilization efficiency while ensuring economic characteristics, demonstrating potential in enhancing user experience and platform revenue.
We present a new encoder-decoder generative network dubbed EdgeNet, which introduces a novel encoder-decoder framework for data-driven auction design in online e-commerce advertising. We break the neural auction paradigm of Generalized-Second-Price(GSP), and improve the utilization efficiency of data while ensuring the economic characteristics of the auction mechanism. Specifically, EdgeNet introduces a transformer-based encoder to better capture the mutual influence among different candidate advertisements. In contrast to GSP based neural auction model, we design an autoregressive decoder to better utilize the rich context information in online advertising auctions. EdgeNet is conceptually simple and easy to extend to the existing end-to-end neural auction framework. We validate the efficiency of EdgeNet on a wide range of e-commercial advertising auction, demonstrating its potential in improving user experience and platform revenue.